GLPK/Julia

The GLPK.jl package allows use of the GLPK in julia in different ways. In an increasing level of abstraction these are: (a) directly (by wrapping the C API); (b) as interface to MathOptInterface or (c) as the "solver engine" of the algebraic modelling language JuMP.jl. For example to solve the problem `Maxx,y x + 2y` s.t. `x+y = 3` and `x,y >=0` you could write: (a) Using the GLPK C API wrapper: using Pkg; Pkg.add("GLPK") # Just once to install the packages using GLPK lp = GLPK.Prob() GLPK.set_obj_dir(lp, GLPK.MAX) GLPK.add_rows(lp, 1) GLPK.set_row_bnds(lp, 1, GLPK.FX, 3.0, 3.0) GLPK.add_cols(lp, 2) GLPK.set_col_bnds(lp, 1, GLPK.LO, 0.0, +Inf) GLPK.set_obj_coef(lp, 1, 1.0) GLPK.set_col_bnds(lp, 2, GLPK.LO, 0.0, +Inf) GLPK.set_obj_coef(lp, 2, 2.0) GLPK.set_mat_row(lp, 1, [1,2], [1,1]) # coefficients given in sparse form param = GLPK.SimplexParam() flag = GLPK.simplex(lp, param) # solves GLPK.get_obj_val(lp) # 6 GLPK.get_col_prim(lp, 1) # 0 GLPK.get_col_prim(lp, 2) # 3 (c) using the JuMP API: using Pkg; Pkg.add(["GLPK","JuMP"]) # Just once to install the packages using GLPK, JuMP model = Model(GLPK.Optimizer) set_optimizer_attribute(model, "msg_lev", GLPK.MSG_OFF) @variables model begin x >= 0 # could be parts of n-dimensional arrays, like x[p in plants, m in markets] y >= 0 end @constraints model begin bound1, x+y == 3 end @objective model Max begin x + 2*y end print(model) optimize!(model) objective_value(model) # 6 value(x) # 0 value(y) # 3